Universität Wien
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052113 VU Software Tools for Computational and Data Science (2026S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work

Diese Lehrveranstaltung ist äquivalent zur VU "Software Tools and Libraries for Scientific Computing"
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 02.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 04.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 09.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 11.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 16.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 18.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 23.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 25.03. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 13.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 15.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 20.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 22.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 27.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 29.04. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 04.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 06.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 11.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 13.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 18.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 20.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 27.05. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 01.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 03.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 08.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 10.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 15.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 17.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 22.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Wednesday 24.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
  • Monday 29.06. 18:30 - 20:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

We discuss software tools for computational and data science in the fields of linear algebra, gradient-based optimization, ordinary differential equations, sparse linear solvers, and neural networks, with a focus on learning-based methods, including their foundational numerical algorithms and GPU-accelerated computation using CUDA.

Students gain hands-on experience with computational software such as BLAS and LAPACK, PyTorch, MPFR, PETSc, and CUDA, and they learn to implement neural network models, automatic differentiation, and gradient-based training pipelines. Computational results are evaluated to ensure optimal performance. Hands-on experience is reinforced through in-depth discussions.

The students are expected to have good general programming skills, basic familiarity with programming in C and Python (other languages upon request), and experience in using GNU/Linux and Bash. This course builds directly upon Introduction to Numerical Computing (NUM) and Combinatorial and Numerical Algorithms (CNA).

Assessment and permitted materials

The grading will be based on two closed-book written exams and individual projects.

Minimum requirements and assessment criteria

Attendance is compulsory during the entire course. Each part (projects and exams, respectively) needs a score of at least 50%; 1 (Excellent): 87.5% and above, 2 (Gut): 75% to 87.4%, 3 (Befriedigend): 62.5% to 74.9%, 4 (Genügend): 50% to 62.4%, 5 (NIcht Genügend): Below 50%.

Examination topics

All topics of the lectures will be relevant for the exams.

Reading list

The lectures are accompanied by slides which point to additional relevant literature (supplied in the course). Textbooks etc. are not required.

Association in the course directory

Module: STL

Last modified: Mo 27.07.2026 09:26